作為一枚機器學習的愛好者，邏輯迴歸算是一個簡單入門的演算法，原理比較簡單，但是自己手動實現邏輯迴歸有一些要注意的事項：

第一是步長選擇的問題，根據你的資料大小來選擇。

第二是自己手動可選擇加不加入常數項，用於做訓練。

第三是實際寫程式碼用的梯度上升程式碼來求解，演算法原理建議使用梯度下降，但是工程為了方便用梯度上升來求解。

第四是正則化問題，可以選擇L1、L2正則來實現你的程式碼。

第五是終止條件的問題，一般寫工程可以選擇迭代次數，也可以選擇根據最後weights變化來寫終止條件，也可以兩個一起結合一起使用。

第六是優化演算法，可以用批梯度，也可以用隨機梯度，也可以擬牛頓迭代法，原理都較簡單。

基本就是這些，歡迎大牛補充，下面自己用java寫了個，資料來源是python機器學習實戰那本書裡面的資料，java實現就麼有用矩陣，瞭解矩陣演算法背後原理實際用list也是一個性質，不說直接看程式碼。

首先是讀取資料程式碼：

package com.wanda.logistic;

import java.io.BufferedReader;
import java.io.FileInputStream;
import java.io.File;
import java.io.IOException;
import java.io.InputStreamReader;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.List;

public class ReadData {
	public static final String PATH = "d:\\wilson.zhou\\Desktop\\logistic.txt";
	public  static List<List<Float>> dataList = new ArrayList<List<Float>>();
	public static List<Float> labelList = new ArrayList<Float>();

	static {
		try {
			init();
		} catch (IOException e) {

			e.printStackTrace();
		}
	}

	private static void init() throws IOException {
		BufferedReader buff = new BufferedReader(new InputStreamReader(
				(new FileInputStream(new File(PATH)))));

		String str = buff.readLine();
		while (str != null) {
			String[] arr = str.split("\t");
			labelList.add(Float.parseFloat(arr[2]));
			dataList.add(Arrays.asList(Float.parseFloat(arr[0]),
					Float.parseFloat(arr[1])));
			str = buff.readLine();
		}
		buff.close();
		
	}

}
 

邏輯迴歸程式碼:

package com.wanda.logistic;

import java.util.Arrays;
import java.util.List;

public class LogRegression {
	public static void main(String[] args) {
		LogRegression lr = new LogRegression();
		ReadData instances = new ReadData();
		lr.train(instances, 0.001f, 1); //
	}

	public void train(ReadData instances, float step, int type) {
		List<List<Float>> datas = instances.dataList;
		List<Float> labels = instances.labelList;
		int size = datas.size();
		int dim = datas.get(0).size();
		float[] w = new float[dim]; // 初始化權重
		float changas = Float.MAX_VALUE;
		int caculate = 0;

		switch (type) {
		case 1: // 批梯度下降的方式
			while (changas > 0.0001) {
				float[] wClone = w.clone();
				float[] out = new float[size];
				for (int s = 0; s < size; s++) {
					float lire = innerProduct(w, datas.get(s));
					out[s] = sigmoid(lire);
				}
				for (int d = 0; d < dim; d++) {
					float sum = 0;
					for (int s = 0; s < size; s++) {
						sum += (labels.get(s) - out[s]) * datas.get(s).get(d);
					}
					float q=w[d];
					w[d] = (float) (q + step * sum);
					
//					w[d] = (float) (q + step * sum-0.01*Math.pow(q,2)); L2正則
//					w[d] = (float) (q + step * sum-0.01*Math.abs(q));  L1正則
				}
				changas = changsWeight(wClone, w);
				caculate++;
				System.out.println("迭代次數是:" + caculate + "  權重是:"
						+ Arrays.toString(w));
			}

			break;
		case 2://隨機梯度下降
			while (changas > 0.0001) {
				float[] wClone = w.clone();
				for (int s = 0; s < size; s++) {
					float lire = innerProduct(w, datas.get(s));
					float out = sigmoid(lire);
					float error = labels.get(s) - out;
					for (int d = 0; d < dim; d++) {
						w[d] += step * error * datas.get(s).get(d);
					}
				}
				changas = changsWeight(wClone, w);
				caculate++;

				System.out.println("迭代次數是:" + caculate + "  權重是:"
						+ Arrays.toString(w));
			}

			break;

		default:
			break;
		}

	}

	private float changsWeight(float[] wClone, float[] w) {
		float changs = 0;
		for (int i = 0; i < w.length; i++) {
			changs += Math.pow(w[i] - wClone[i], 2);
		}

		return (float) Math.sqrt(changs);

	}

	private float innerProduct(float[] w, List<Float> x) {
		float sum = 0;
		for (int i = 0; i < w.length; i++) {
			sum += w[i] * x.get(i);
		}

		return sum;
	}

	private float sigmoid(float src) {
		return (float) (1.0 / (1 + Math.exp(-src)));
	}

}
 

資料：

-0.017612	14.053064	0
-1.395634	4.662541	1
-0.752157	6.538620	0
-1.322371	7.152853	0
0.423363	11.054677	0
0.406704	7.067335	1
0.667394	12.741452	0
-2.460150	6.866805	1
0.569411	9.548755	0
-0.026632	10.427743	0
0.850433	6.920334	1
1.347183	13.175500	0
1.176813	3.167020	1
-1.781871	9.097953	0
-0.566606	5.749003	1
0.931635	1.589505	1
-0.024205	6.151823	1
-0.036453	2.690988	1
-0.196949	0.444165	1
1.014459	5.754399	1
1.985298	3.230619	1
-1.693453	-0.557540	1
-0.576525	11.778922	0
-0.346811	-1.678730	1
-2.124484	2.672471	1
1.217916	9.597015	0
-0.733928	9.098687	0
-3.642001	-1.618087	1
0.315985	3.523953	1
1.416614	9.619232	0
-0.386323	3.989286	1
0.556921	8.294984	1
1.224863	11.587360	0
-1.347803	-2.406051	1
1.196604	4.951851	1
0.275221	9.543647	0
0.470575	9.332488	0
-1.889567	9.542662	0
-1.527893	12.150579	0
-1.185247	11.309318	0
-0.445678	3.297303	1
1.042222	6.105155	1
-0.618787	10.320986	0
1.152083	0.548467	1
0.828534	2.676045	1
-1.237728	10.549033	0
-0.683565	-2.166125	1
0.229456	5.921938	1
-0.959885	11.555336	0
0.492911	10.993324	0
0.184992	8.721488	0
-0.355715	10.325976	0
-0.397822	8.058397	0
0.824839	13.730343	0
1.507278	5.027866	1
0.099671	6.835839	1
-0.344008	10.717485	0
1.785928	7.718645	1
-0.918801	11.560217	0
-0.364009	4.747300	1
-0.841722	4.119083	1
0.490426	1.960539	1
-0.007194	9.075792	0
0.356107	12.447863	0
0.342578	12.281162	0
-0.810823	-1.466018	1
2.530777	6.476801	1
1.296683	11.607559	0
0.475487	12.040035	0
-0.783277	11.009725	0
0.074798	11.023650	0
-1.337472	0.468339	1
-0.102781	13.763651	0
-0.147324	2.874846	1
0.518389	9.887035	0
1.015399	7.571882	0
-1.658086	-0.027255	1
1.319944	2.171228	1
2.056216	5.019981	1
-0.851633	4.375691	1
-1.510047	6.061992	0
-1.076637	-3.181888	1
1.821096	10.283990	0
3.010150	8.401766	1
-1.099458	1.688274	1
-0.834872	-1.733869	1
-0.846637	3.849075	1
1.400102	12.628781	0
1.752842	5.468166	1
0.078557	0.059736	1
0.089392	-0.715300	1
1.825662	12.693808	0
0.197445	9.744638	0
0.126117	0.922311	1
-0.679797	1.220530	1
0.677983	2.556666	1
0.761349	10.693862	0
-2.168791	0.143632	1
1.388610	9.341997	0
0.317029	14.739025	0